Overview

Dataset statistics

Number of variables28
Number of observations36992
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 MiB
Average record size in memory212.0 B

Variable types

Text5
Numeric11
Categorical9
Boolean3

Alerts

points_in_wallet is highly overall correlated with churn_risk_scoreHigh correlation
churn_risk_score is highly overall correlated with points_in_walletHigh correlation
used_special_discount is highly overall correlated with offer_application_preferenceHigh correlation
offer_application_preference is highly overall correlated with used_special_discountHigh correlation
customer_id has unique valuesUnique
Name has unique valuesUnique
security_no has unique valuesUnique
last_visit_time_hour has 1512 (4.1%) zerosZeros
last_visit_time_minutes has 645 (1.7%) zerosZeros
last_visit_time_seconds has 630 (1.7%) zerosZeros

Reproduction

Analysis started2023-09-19 06:23:40.951460
Analysis finished2023-09-19 06:23:59.227549
Duration18.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

customer_id
Text

UNIQUE 

Distinct36992
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
2023-09-19T11:53:59.404850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length36
Median length36
Mean length35.271734
Min length20

Characters and Unicode

Total characters1304772
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36992 ?
Unique (%)100.0%

Sample

1st rowfffe4300490044003600300030003800
2nd rowfffe43004900440032003100300035003700
3rd rowfffe4300490044003100390032003600
4th rowfffe43004900440036003000330031003600
5th rowfffe43004900440031003900350030003600
ValueCountFrequency (%)
fffe4300490044003600300030003800 1
 
< 0.1%
fffe43004900440033003700330038003900 1
 
< 0.1%
fffe43004900440032003200350033003200 1
 
< 0.1%
fffe43004900440033003300330032003200 1
 
< 0.1%
fffe4300490044003100390032003600 1
 
< 0.1%
fffe43004900440036003000330031003600 1
 
< 0.1%
fffe43004900440031003900350030003600 1
 
< 0.1%
fffe43004900440036003300320035003300 1
 
< 0.1%
fffe43004900440031003100360037003900 1
 
< 0.1%
fffe4300490044003800300035003800 1
 
< 0.1%
Other values (36982) 36982
> 99.9%
2023-09-19T11:53:59.686417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 592862
45.4%
3 236016
 
18.1%
4 168252
 
12.9%
f 110976
 
8.5%
9 51579
 
4.0%
e 36992
 
2.8%
1 20900
 
1.6%
2 20831
 
1.6%
5 20484
 
1.6%
6 16674
 
1.3%
Other values (2) 29206
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1156804
88.7%
Lowercase Letter 147968
 
11.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 592862
51.2%
3 236016
 
20.4%
4 168252
 
14.5%
9 51579
 
4.5%
1 20900
 
1.8%
2 20831
 
1.8%
5 20484
 
1.8%
6 16674
 
1.4%
8 14641
 
1.3%
7 14565
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
f 110976
75.0%
e 36992
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1156804
88.7%
Latin 147968
 
11.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 592862
51.2%
3 236016
 
20.4%
4 168252
 
14.5%
9 51579
 
4.5%
1 20900
 
1.8%
2 20831
 
1.8%
5 20484
 
1.8%
6 16674
 
1.4%
8 14641
 
1.3%
7 14565
 
1.3%
Latin
ValueCountFrequency (%)
f 110976
75.0%
e 36992
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1304772
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 592862
45.4%
3 236016
 
18.1%
4 168252
 
12.9%
f 110976
 
8.5%
9 51579
 
4.0%
e 36992
 
2.8%
1 20900
 
1.6%
2 20831
 
1.6%
5 20484
 
1.6%
6 16674
 
1.3%
Other values (2) 29206
 
2.2%

Name
Text

UNIQUE 

Distinct36992
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
2023-09-19T11:53:59.862891image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length24
Median length21
Mean length13.522924
Min length6

Characters and Unicode

Total characters500240
Distinct characters53
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36992 ?
Unique (%)100.0%

Sample

1st rowPattie Morrisey
2nd rowTraci Peery
3rd rowMerideth Mcmeen
4th rowEufemia Cardwell
5th rowMeghan Kosak
ValueCountFrequency (%)
sidney 137
 
0.2%
gilda 126
 
0.2%
selena 123
 
0.2%
noe 123
 
0.2%
lesli 121
 
0.2%
karri 121
 
0.2%
marietta 120
 
0.2%
kenny 120
 
0.2%
earlie 119
 
0.2%
dean 119
 
0.2%
Other values (2511) 72755
98.3%
2023-09-19T11:54:00.151967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 54599
 
10.9%
a 50451
 
10.1%
36992
 
7.4%
n 36286
 
7.3%
r 34094
 
6.8%
i 33029
 
6.6%
l 29840
 
6.0%
o 24859
 
5.0%
t 17988
 
3.6%
s 15985
 
3.2%
Other values (43) 166117
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 389264
77.8%
Uppercase Letter 73984
 
14.8%
Space Separator 36992
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 54599
14.0%
a 50451
13.0%
n 36286
9.3%
r 34094
8.8%
i 33029
8.5%
l 29840
 
7.7%
o 24859
 
6.4%
t 17988
 
4.6%
s 15985
 
4.1%
d 11744
 
3.0%
Other values (16) 80389
20.7%
Uppercase Letter
ValueCountFrequency (%)
S 7104
 
9.6%
M 6774
 
9.2%
L 5702
 
7.7%
B 4974
 
6.7%
A 4941
 
6.7%
C 4690
 
6.3%
D 3984
 
5.4%
K 3882
 
5.2%
G 3507
 
4.7%
R 3447
 
4.7%
Other values (16) 24979
33.8%
Space Separator
ValueCountFrequency (%)
36992
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 463248
92.6%
Common 36992
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 54599
 
11.8%
a 50451
 
10.9%
n 36286
 
7.8%
r 34094
 
7.4%
i 33029
 
7.1%
l 29840
 
6.4%
o 24859
 
5.4%
t 17988
 
3.9%
s 15985
 
3.5%
d 11744
 
2.5%
Other values (42) 154373
33.3%
Common
ValueCountFrequency (%)
36992
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 500240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 54599
 
10.9%
a 50451
 
10.1%
36992
 
7.4%
n 36286
 
7.3%
r 34094
 
6.8%
i 33029
 
6.6%
l 29840
 
6.0%
o 24859
 
5.0%
t 17988
 
3.6%
s 15985
 
3.2%
Other values (43) 166117
33.2%

age
Real number (ℝ)

Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.118161
Minimum10
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size289.1 KiB
2023-09-19T11:54:00.280357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12
Q123
median37
Q351
95-th percentile62
Maximum64
Range54
Interquartile range (IQR)28

Descriptive statistics

Standard deviation15.867412
Coefficient of variation (CV)0.4274838
Kurtosis-1.1987327
Mean37.118161
Median Absolute Deviation (MAD)14
Skewness-0.0073193193
Sum1373075
Variance251.77477
MonotonicityNot monotonic
2023-09-19T11:54:00.408960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 720
 
1.9%
42 716
 
1.9%
16 716
 
1.9%
38 714
 
1.9%
30 711
 
1.9%
61 709
 
1.9%
60 704
 
1.9%
57 704
 
1.9%
41 699
 
1.9%
59 696
 
1.9%
Other values (45) 29903
80.8%
ValueCountFrequency (%)
10 670
1.8%
11 654
1.8%
12 661
1.8%
13 654
1.8%
14 670
1.8%
15 649
1.8%
16 716
1.9%
17 683
1.8%
18 629
1.7%
19 660
1.8%
ValueCountFrequency (%)
64 672
1.8%
63 656
1.8%
62 677
1.8%
61 709
1.9%
60 704
1.9%
59 696
1.9%
58 678
1.8%
57 704
1.9%
56 682
1.8%
55 695
1.9%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
F
18490 
M
18443 
Unknown
 
59

Length

Max length7
Median length1
Mean length1.0095696
Min length1

Characters and Unicode

Total characters37346
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
F 18490
50.0%
M 18443
49.9%
Unknown 59
 
0.2%

Length

2023-09-19T11:54:00.514410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T11:54:00.611587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
f 18490
50.0%
m 18443
49.9%
unknown 59
 
0.2%

Most occurring characters

ValueCountFrequency (%)
F 18490
49.5%
M 18443
49.4%
n 177
 
0.5%
U 59
 
0.2%
k 59
 
0.2%
o 59
 
0.2%
w 59
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 36992
99.1%
Lowercase Letter 354
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 177
50.0%
k 59
 
16.7%
o 59
 
16.7%
w 59
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
F 18490
50.0%
M 18443
49.9%
U 59
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 37346
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 18490
49.5%
M 18443
49.4%
n 177
 
0.5%
U 59
 
0.2%
k 59
 
0.2%
o 59
 
0.2%
w 59
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 18490
49.5%
M 18443
49.4%
n 177
 
0.5%
U 59
 
0.2%
k 59
 
0.2%
o 59
 
0.2%
w 59
 
0.2%

security_no
Text

UNIQUE 

Distinct36992
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
2023-09-19T11:54:00.788435image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters258944
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36992 ?
Unique (%)100.0%

Sample

1st rowXW0DQ7H
2nd row5K0N3X1
3rd row1F2TCL3
4th rowVJGJ33N
5th rowSVZXCWB
ValueCountFrequency (%)
xw0dq7h 1
 
< 0.1%
6rz86vw 1
 
< 0.1%
c229qzz 1
 
< 0.1%
0481qnq 1
 
< 0.1%
1f2tcl3 1
 
< 0.1%
vjgj33n 1
 
< 0.1%
svzxcwb 1
 
< 0.1%
psg1lgf 1
 
< 0.1%
r3cx1ea 1
 
< 0.1%
4uj1551 1
 
< 0.1%
Other values (36982) 36982
> 99.9%
2023-09-19T11:54:01.077467image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
G 7410
 
2.9%
D 7391
 
2.9%
H 7346
 
2.8%
Z 7307
 
2.8%
A 7294
 
2.8%
T 7270
 
2.8%
F 7262
 
2.8%
R 7249
 
2.8%
M 7248
 
2.8%
5 7244
 
2.8%
Other values (26) 185923
71.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 187293
72.3%
Decimal Number 71651
 
27.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 7410
 
4.0%
D 7391
 
3.9%
H 7346
 
3.9%
Z 7307
 
3.9%
A 7294
 
3.9%
T 7270
 
3.9%
F 7262
 
3.9%
R 7249
 
3.9%
M 7248
 
3.9%
O 7229
 
3.9%
Other values (16) 114287
61.0%
Decimal Number
ValueCountFrequency (%)
5 7244
10.1%
2 7236
10.1%
7 7220
10.1%
8 7213
10.1%
9 7171
10.0%
4 7171
10.0%
1 7163
10.0%
0 7157
10.0%
3 7043
9.8%
6 7033
9.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 187293
72.3%
Common 71651
 
27.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 7410
 
4.0%
D 7391
 
3.9%
H 7346
 
3.9%
Z 7307
 
3.9%
A 7294
 
3.9%
T 7270
 
3.9%
F 7262
 
3.9%
R 7249
 
3.9%
M 7248
 
3.9%
O 7229
 
3.9%
Other values (16) 114287
61.0%
Common
ValueCountFrequency (%)
5 7244
10.1%
2 7236
10.1%
7 7220
10.1%
8 7213
10.1%
9 7171
10.0%
4 7171
10.0%
1 7163
10.0%
0 7157
10.0%
3 7043
9.8%
6 7033
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 258944
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 7410
 
2.9%
D 7391
 
2.9%
H 7346
 
2.8%
Z 7307
 
2.8%
A 7294
 
2.8%
T 7270
 
2.8%
F 7262
 
2.8%
R 7249
 
2.8%
M 7248
 
2.8%
5 7244
 
2.8%
Other values (26) 185923
71.8%

region_category
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Town
19556 
City
12737 
Village
4699 

Length

Max length7
Median length4
Mean length4.3810824
Min length4

Characters and Unicode

Total characters162065
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVillage
2nd rowCity
3rd rowTown
4th rowCity
5th rowCity

Common Values

ValueCountFrequency (%)
Town 19556
52.9%
City 12737
34.4%
Village 4699
 
12.7%

Length

2023-09-19T11:54:01.205163image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T11:54:01.293250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
town 19556
52.9%
city 12737
34.4%
village 4699
 
12.7%

Most occurring characters

ValueCountFrequency (%)
T 19556
12.1%
o 19556
12.1%
w 19556
12.1%
n 19556
12.1%
i 17436
10.8%
C 12737
7.9%
t 12737
7.9%
y 12737
7.9%
l 9398
5.8%
V 4699
 
2.9%
Other values (3) 14097
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 125073
77.2%
Uppercase Letter 36992
 
22.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 19556
15.6%
w 19556
15.6%
n 19556
15.6%
i 17436
13.9%
t 12737
10.2%
y 12737
10.2%
l 9398
7.5%
a 4699
 
3.8%
g 4699
 
3.8%
e 4699
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
T 19556
52.9%
C 12737
34.4%
V 4699
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 162065
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 19556
12.1%
o 19556
12.1%
w 19556
12.1%
n 19556
12.1%
i 17436
10.8%
C 12737
7.9%
t 12737
7.9%
y 12737
7.9%
l 9398
5.8%
V 4699
 
2.9%
Other values (3) 14097
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162065
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 19556
12.1%
o 19556
12.1%
w 19556
12.1%
n 19556
12.1%
i 17436
10.8%
C 12737
7.9%
t 12737
7.9%
y 12737
7.9%
l 9398
5.8%
V 4699
 
2.9%
Other values (3) 14097
8.7%

membership_category
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Basic Membership
7724 
No Membership
7692 
Gold Membership
6795 
Silver Membership
5988 
Premium Membership
4455 

Length

Max length19
Median length17
Mean length15.947043
Min length13

Characters and Unicode

Total characters589913
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPlatinum Membership
2nd rowPremium Membership
3rd rowNo Membership
4th rowNo Membership
5th rowNo Membership

Common Values

ValueCountFrequency (%)
Basic Membership 7724
20.9%
No Membership 7692
20.8%
Gold Membership 6795
18.4%
Silver Membership 5988
16.2%
Premium Membership 4455
12.0%
Platinum Membership 4338
11.7%

Length

2023-09-19T11:54:01.389562image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T11:54:01.495930image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
membership 36992
50.0%
basic 7724
 
10.4%
no 7692
 
10.4%
gold 6795
 
9.2%
silver 5988
 
8.1%
premium 4455
 
6.0%
platinum 4338
 
5.9%

Most occurring characters

ValueCountFrequency (%)
e 84427
14.3%
i 59497
10.1%
m 50240
8.5%
r 47435
 
8.0%
s 44716
 
7.6%
p 36992
 
6.3%
36992
 
6.3%
M 36992
 
6.3%
b 36992
 
6.3%
h 36992
 
6.3%
Other values (14) 118638
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 478937
81.2%
Uppercase Letter 73984
 
12.5%
Space Separator 36992
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 84427
17.6%
i 59497
12.4%
m 50240
10.5%
r 47435
9.9%
s 44716
9.3%
p 36992
7.7%
b 36992
7.7%
h 36992
7.7%
l 17121
 
3.6%
o 14487
 
3.0%
Other values (7) 50038
10.4%
Uppercase Letter
ValueCountFrequency (%)
M 36992
50.0%
P 8793
 
11.9%
B 7724
 
10.4%
N 7692
 
10.4%
G 6795
 
9.2%
S 5988
 
8.1%
Space Separator
ValueCountFrequency (%)
36992
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 552921
93.7%
Common 36992
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 84427
15.3%
i 59497
10.8%
m 50240
9.1%
r 47435
8.6%
s 44716
8.1%
p 36992
 
6.7%
M 36992
 
6.7%
b 36992
 
6.7%
h 36992
 
6.7%
l 17121
 
3.1%
Other values (13) 101517
18.4%
Common
ValueCountFrequency (%)
36992
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 589913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 84427
14.3%
i 59497
10.1%
m 50240
8.5%
r 47435
 
8.0%
s 44716
 
7.6%
p 36992
 
6.3%
36992
 
6.3%
M 36992
 
6.3%
b 36992
 
6.3%
h 36992
 
6.3%
Other values (14) 118638
20.1%

joined_through_referral
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
No
15839 
Yes
15715 
?
5438 

Length

Max length3
Median length2
Mean length2.2778168
Min length1

Characters and Unicode

Total characters84261
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd row?
3rd rowYes
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 15839
42.8%
Yes 15715
42.5%
? 5438
 
14.7%

Length

2023-09-19T11:54:01.623790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T11:54:01.720146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
no 15839
42.8%
yes 15715
42.5%
5438
 
14.7%

Most occurring characters

ValueCountFrequency (%)
N 15839
18.8%
o 15839
18.8%
Y 15715
18.7%
e 15715
18.7%
s 15715
18.7%
? 5438
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 47269
56.1%
Uppercase Letter 31554
37.4%
Other Punctuation 5438
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 15839
33.5%
e 15715
33.2%
s 15715
33.2%
Uppercase Letter
ValueCountFrequency (%)
N 15839
50.2%
Y 15715
49.8%
Other Punctuation
ValueCountFrequency (%)
? 5438
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 78823
93.5%
Common 5438
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 15839
20.1%
o 15839
20.1%
Y 15715
19.9%
e 15715
19.9%
s 15715
19.9%
Common
ValueCountFrequency (%)
? 5438
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84261
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 15839
18.8%
o 15839
18.8%
Y 15715
18.7%
e 15715
18.7%
s 15715
18.7%
? 5438
 
6.5%

referral_id
Text

Distinct11359
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
2023-09-19T11:54:01.862097image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length11
Median length8
Mean length7.9114944
Min length4

Characters and Unicode

Total characters292662
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6801 ?
Unique (%)18.4%

Sample

1st rowxxxxxxxx
2nd rowCID21329
3rd rowCID12313
4th rowCID3793
5th rowxxxxxxxx
ValueCountFrequency (%)
xxxxxxxx 17846
48.2%
cid43705 12
 
< 0.1%
cid3979 11
 
< 0.1%
cid49601 10
 
< 0.1%
cid15792 9
 
< 0.1%
cid23978 9
 
< 0.1%
cid40797 9
 
< 0.1%
cid49598 9
 
< 0.1%
cid62015 9
 
< 0.1%
cid43428 8
 
< 0.1%
Other values (11350) 19065
51.5%
2023-09-19T11:54:02.140825image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
x 142768
48.8%
C 19141
 
6.5%
I 19141
 
6.5%
D 19141
 
6.5%
1 10838
 
3.7%
2 10822
 
3.7%
3 10766
 
3.7%
5 10537
 
3.6%
4 10515
 
3.6%
6 8761
 
3.0%
Other values (12) 30232
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 142813
48.8%
Decimal Number 92416
31.6%
Uppercase Letter 57428
19.6%
Space Separator 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10838
11.7%
2 10822
11.7%
3 10766
11.6%
5 10537
11.4%
4 10515
11.4%
6 8761
9.5%
0 7591
8.2%
8 7559
8.2%
9 7550
8.2%
7 7477
8.1%
Lowercase Letter
ValueCountFrequency (%)
x 142768
> 99.9%
r 15
 
< 0.1%
e 10
 
< 0.1%
o 5
 
< 0.1%
f 5
 
< 0.1%
a 5
 
< 0.1%
l 5
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
C 19141
33.3%
I 19141
33.3%
D 19141
33.3%
N 5
 
< 0.1%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 200241
68.4%
Common 92421
31.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
x 142768
71.3%
C 19141
 
9.6%
I 19141
 
9.6%
D 19141
 
9.6%
r 15
 
< 0.1%
e 10
 
< 0.1%
N 5
 
< 0.1%
o 5
 
< 0.1%
f 5
 
< 0.1%
a 5
 
< 0.1%
Common
ValueCountFrequency (%)
1 10838
11.7%
2 10822
11.7%
3 10766
11.6%
5 10537
11.4%
4 10515
11.4%
6 8761
9.5%
0 7591
8.2%
8 7559
8.2%
9 7550
8.2%
7 7477
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 292662
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
x 142768
48.8%
C 19141
 
6.5%
I 19141
 
6.5%
D 19141
 
6.5%
1 10838
 
3.7%
2 10822
 
3.7%
3 10766
 
3.7%
5 10537
 
3.6%
4 10515
 
3.6%
6 8761
 
3.0%
Other values (12) 30232
 
10.3%

preferred_offer_types
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Gift Vouchers/Coupons
12637 
Credit/Debit Card Offers
12274 
Without Offers
12081 

Length

Max length24
Median length21
Mean length19.709316
Min length14

Characters and Unicode

Total characters729087
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGift Vouchers/Coupons
2nd rowGift Vouchers/Coupons
3rd rowGift Vouchers/Coupons
4th rowGift Vouchers/Coupons
5th rowCredit/Debit Card Offers

Common Values

ValueCountFrequency (%)
Gift Vouchers/Coupons 12637
34.2%
Credit/Debit Card Offers 12274
33.2%
Without Offers 12081
32.7%

Length

2023-09-19T11:54:02.260659image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T11:54:02.348590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
offers 24355
28.2%
gift 12637
14.7%
vouchers/coupons 12637
14.7%
credit/debit 12274
14.2%
card 12274
14.2%
without 12081
14.0%

Most occurring characters

ValueCountFrequency (%)
r 61540
 
8.4%
e 61540
 
8.4%
f 61347
 
8.4%
t 61347
 
8.4%
o 49992
 
6.9%
s 49629
 
6.8%
49266
 
6.8%
i 49266
 
6.8%
u 37355
 
5.1%
C 37185
 
5.1%
Other values (13) 210620
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 543741
74.6%
Uppercase Letter 111169
 
15.2%
Space Separator 49266
 
6.8%
Other Punctuation 24911
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 61540
11.3%
e 61540
11.3%
f 61347
11.3%
t 61347
11.3%
o 49992
9.2%
s 49629
9.1%
i 49266
9.1%
u 37355
6.9%
h 24718
 
4.5%
d 24548
 
4.5%
Other values (5) 62459
11.5%
Uppercase Letter
ValueCountFrequency (%)
C 37185
33.4%
O 24355
21.9%
G 12637
 
11.4%
V 12637
 
11.4%
D 12274
 
11.0%
W 12081
 
10.9%
Space Separator
ValueCountFrequency (%)
49266
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 24911
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 654910
89.8%
Common 74177
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 61540
 
9.4%
e 61540
 
9.4%
f 61347
 
9.4%
t 61347
 
9.4%
o 49992
 
7.6%
s 49629
 
7.6%
i 49266
 
7.5%
u 37355
 
5.7%
C 37185
 
5.7%
h 24718
 
3.8%
Other values (11) 160991
24.6%
Common
ValueCountFrequency (%)
49266
66.4%
/ 24911
33.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 729087
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 61540
 
8.4%
e 61540
 
8.4%
f 61347
 
8.4%
t 61347
 
8.4%
o 49992
 
6.9%
s 49629
 
6.8%
49266
 
6.8%
i 49266
 
6.8%
u 37355
 
5.1%
C 37185
 
5.1%
Other values (13) 210620
28.9%

medium_of_operation
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Desktop
13913 
Smartphone
13876 
?
5393 
Both
3810 

Length

Max length10
Median length7
Mean length6.941609
Min length1

Characters and Unicode

Total characters256784
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd rowDesktop
3rd rowDesktop
4th rowDesktop
5th rowSmartphone

Common Values

ValueCountFrequency (%)
Desktop 13913
37.6%
Smartphone 13876
37.5%
? 5393
 
14.6%
Both 3810
 
10.3%

Length

2023-09-19T11:54:02.444987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T11:54:02.541133image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
desktop 13913
37.6%
smartphone 13876
37.5%
5393
 
14.6%
both 3810
 
10.3%

Most occurring characters

ValueCountFrequency (%)
t 31599
12.3%
o 31599
12.3%
e 27789
10.8%
p 27789
10.8%
h 17686
 
6.9%
D 13913
 
5.4%
s 13913
 
5.4%
k 13913
 
5.4%
S 13876
 
5.4%
m 13876
 
5.4%
Other values (5) 50831
19.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 219792
85.6%
Uppercase Letter 31599
 
12.3%
Other Punctuation 5393
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 31599
14.4%
o 31599
14.4%
e 27789
12.6%
p 27789
12.6%
h 17686
8.0%
s 13913
6.3%
k 13913
6.3%
m 13876
6.3%
a 13876
6.3%
r 13876
6.3%
Uppercase Letter
ValueCountFrequency (%)
D 13913
44.0%
S 13876
43.9%
B 3810
 
12.1%
Other Punctuation
ValueCountFrequency (%)
? 5393
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 251391
97.9%
Common 5393
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 31599
12.6%
o 31599
12.6%
e 27789
11.1%
p 27789
11.1%
h 17686
 
7.0%
D 13913
 
5.5%
s 13913
 
5.5%
k 13913
 
5.5%
S 13876
 
5.5%
m 13876
 
5.5%
Other values (4) 45438
18.1%
Common
ValueCountFrequency (%)
? 5393
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 256784
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 31599
12.3%
o 31599
12.3%
e 27789
10.8%
p 27789
10.8%
h 17686
 
6.9%
D 13913
 
5.4%
s 13913
 
5.4%
k 13913
 
5.4%
S 13876
 
5.4%
m 13876
 
5.4%
Other values (5) 50831
19.8%

internet_option
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Wi-Fi
12413 
Mobile_Data
12343 
Fiber_Optic
12236 

Length

Max length11
Median length11
Mean length8.9866458
Min length5

Characters and Unicode

Total characters332434
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWi-Fi
2nd rowMobile_Data
3rd rowWi-Fi
4th rowMobile_Data
5th rowMobile_Data

Common Values

ValueCountFrequency (%)
Wi-Fi 12413
33.6%
Mobile_Data 12343
33.4%
Fiber_Optic 12236
33.1%

Length

2023-09-19T11:54:02.637223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T11:54:02.725473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
wi-fi 12413
33.6%
mobile_data 12343
33.4%
fiber_optic 12236
33.1%

Most occurring characters

ValueCountFrequency (%)
i 61641
18.5%
a 24686
 
7.4%
F 24649
 
7.4%
e 24579
 
7.4%
t 24579
 
7.4%
_ 24579
 
7.4%
b 24579
 
7.4%
W 12413
 
3.7%
- 12413
 
3.7%
l 12343
 
3.7%
Other values (7) 85973
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 221458
66.6%
Uppercase Letter 73984
 
22.3%
Connector Punctuation 24579
 
7.4%
Dash Punctuation 12413
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 61641
27.8%
a 24686
11.1%
e 24579
 
11.1%
t 24579
 
11.1%
b 24579
 
11.1%
l 12343
 
5.6%
o 12343
 
5.6%
r 12236
 
5.5%
p 12236
 
5.5%
c 12236
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
F 24649
33.3%
W 12413
16.8%
D 12343
16.7%
M 12343
16.7%
O 12236
16.5%
Connector Punctuation
ValueCountFrequency (%)
_ 24579
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12413
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 295442
88.9%
Common 36992
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 61641
20.9%
a 24686
8.4%
F 24649
 
8.3%
e 24579
 
8.3%
t 24579
 
8.3%
b 24579
 
8.3%
W 12413
 
4.2%
l 12343
 
4.2%
o 12343
 
4.2%
D 12343
 
4.2%
Other values (5) 61287
20.7%
Common
ValueCountFrequency (%)
_ 24579
66.4%
- 12413
33.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 332434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 61641
18.5%
a 24686
 
7.4%
F 24649
 
7.4%
e 24579
 
7.4%
t 24579
 
7.4%
_ 24579
 
7.4%
b 24579
 
7.4%
W 12413
 
3.7%
- 12413
 
3.7%
l 12343
 
3.7%
Other values (7) 85973
25.9%

days_since_last_login
Real number (ℝ)

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-41.915576
Minimum-999
Maximum26
Zeros0
Zeros (%)0.0%
Negative1999
Negative (%)5.4%
Memory size289.1 KiB
2023-09-19T11:54:02.821055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q18
median12
Q316
95-th percentile22
Maximum26
Range1025
Interquartile range (IQR)8

Descriptive statistics

Standard deviation228.8199
Coefficient of variation (CV)-5.4590661
Kurtosis13.545985
Mean-41.915576
Median Absolute Deviation (MAD)4
Skewness-3.9413558
Sum-1550541
Variance52358.547
MonotonicityNot monotonic
2023-09-19T11:54:02.925070image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
12 2380
 
6.4%
13 2373
 
6.4%
14 2307
 
6.2%
15 2278
 
6.2%
11 2262
 
6.1%
10 2091
 
5.7%
16 2068
 
5.6%
-999 1999
 
5.4%
9 1863
 
5.0%
17 1747
 
4.7%
Other values (17) 15624
42.2%
ValueCountFrequency (%)
-999 1999
5.4%
1 328
 
0.9%
2 613
 
1.7%
3 852
2.3%
4 998
2.7%
5 1234
3.3%
6 1257
3.4%
7 1442
3.9%
8 1571
4.2%
9 1863
5.0%
ValueCountFrequency (%)
26 82
 
0.2%
25 203
 
0.5%
24 471
 
1.3%
23 727
2.0%
22 895
2.4%
21 1015
2.7%
20 1184
3.2%
19 1308
3.5%
18 1444
3.9%
17 1747
4.7%

avg_time_spent
Real number (ℝ)

Distinct25961
Distinct (%)70.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.47233
Minimum-2814.1091
Maximum3235.5785
Zeros0
Zeros (%)0.0%
Negative1719
Negative (%)4.6%
Memory size289.1 KiB
2023-09-19T11:54:03.029446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-2814.1091
5-th percentile30.15
Q160.1025
median161.765
Q3356.515
95-th percentile1031.0767
Maximum3235.5785
Range6049.6876
Interquartile range (IQR)296.4125

Descriptive statistics

Standard deviation398.28915
Coefficient of variation (CV)1.6358703
Kurtosis5.0039153
Mean243.47233
Median Absolute Deviation (MAD)122.88
Skewness0.53962402
Sum9006528.6
Variance158634.25
MonotonicityNot monotonic
2023-09-19T11:54:03.141786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.1 21
 
0.1%
34.71 20
 
0.1%
33.68 20
 
0.1%
34.33 19
 
0.1%
31.49 18
 
< 0.1%
33.28 18
 
< 0.1%
32.91 18
 
< 0.1%
30.56 18
 
< 0.1%
33.71 18
 
< 0.1%
32.96 17
 
< 0.1%
Other values (25951) 36805
99.5%
ValueCountFrequency (%)
-2814.10911 1
< 0.1%
-2281.236526 1
< 0.1%
-2096.580681 1
< 0.1%
-2093.121606 1
< 0.1%
-2034.80188 1
< 0.1%
-2012.267374 1
< 0.1%
-1960.479169 1
< 0.1%
-1941.035419 1
< 0.1%
-1918.486339 1
< 0.1%
-1913.405154 1
< 0.1%
ValueCountFrequency (%)
3235.578521 1
< 0.1%
3040.41 1
< 0.1%
2899.66 1
< 0.1%
2861.23 1
< 0.1%
2770.56 1
< 0.1%
2766.75 1
< 0.1%
2747.89134 1
< 0.1%
2732.7 1
< 0.1%
2722.077794 1
< 0.1%
2705.756608 1
< 0.1%

avg_transaction_value
Real number (ℝ)

Distinct36894
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29271.194
Minimum800.46
Maximum99914.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size289.1 KiB
2023-09-19T11:54:03.254733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum800.46
5-th percentile3468.9665
Q114177.54
median27554.485
Q340855.11
95-th percentile67338.889
Maximum99914.05
Range99113.59
Interquartile range (IQR)26677.57

Descriptive statistics

Standard deviation19444.806
Coefficient of variation (CV)0.66429836
Kurtosis1.428287
Mean29271.194
Median Absolute Deviation (MAD)13336.775
Skewness1.0110272
Sum1.0828 × 109
Variance3.7810049 × 108
MonotonicityNot monotonic
2023-09-19T11:54:03.374303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14176.97 2
 
< 0.1%
7282.58 2
 
< 0.1%
30126.02 2
 
< 0.1%
21244.03 2
 
< 0.1%
23142.51 2
 
< 0.1%
35460.38 2
 
< 0.1%
9341.33 2
 
< 0.1%
34143.6 2
 
< 0.1%
3432.73 2
 
< 0.1%
6801.07 2
 
< 0.1%
Other values (36884) 36972
99.9%
ValueCountFrequency (%)
800.46 1
< 0.1%
804.34 1
< 0.1%
806.22 1
< 0.1%
806.71 1
< 0.1%
813.82 1
< 0.1%
815.22 1
< 0.1%
821.83 1
< 0.1%
822.7 1
< 0.1%
823.49 1
< 0.1%
823.68 1
< 0.1%
ValueCountFrequency (%)
99914.05 1
< 0.1%
99861.47 1
< 0.1%
99858.02 1
< 0.1%
99819.19 1
< 0.1%
99810.83 1
< 0.1%
99805.52 1
< 0.1%
99803.53 1
< 0.1%
99795.75 1
< 0.1%
99742.63 1
< 0.1%
99730.17 1
< 0.1%

avg_frequency_login_days
Text

Distinct1654
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
2023-09-19T11:54:03.518757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length21
Median length4
Mean length4.494215
Min length3

Characters and Unicode

Total characters166250
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1623 ?
Unique (%)4.4%

Sample

1st row17.0
2nd row10.0
3rd row22.0
4th row6.0
5th row16.0
ValueCountFrequency (%)
error 3522
 
9.5%
13.0 1394
 
3.8%
19.0 1365
 
3.7%
8.0 1361
 
3.7%
14.0 1355
 
3.7%
17.0 1349
 
3.6%
6.0 1336
 
3.6%
10.0 1334
 
3.6%
18.0 1331
 
3.6%
12.0 1327
 
3.6%
Other values (1644) 21318
57.6%
2023-09-19T11:54:03.769286image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 36699
22.1%
. 33470
20.1%
1 18719
11.3%
2 16061
9.7%
r 10566
 
6.4%
5 6508
 
3.9%
7 6331
 
3.8%
6 6312
 
3.8%
8 6269
 
3.8%
9 6258
 
3.8%
Other values (5) 19057
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 114487
68.9%
Other Punctuation 33470
 
20.1%
Lowercase Letter 14088
 
8.5%
Uppercase Letter 3522
 
2.1%
Dash Punctuation 683
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36699
32.1%
1 18719
16.4%
2 16061
14.0%
5 6508
 
5.7%
7 6331
 
5.5%
6 6312
 
5.5%
8 6269
 
5.5%
9 6258
 
5.5%
3 5692
 
5.0%
4 5638
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
r 10566
75.0%
o 3522
 
25.0%
Other Punctuation
ValueCountFrequency (%)
. 33470
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 3522
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 683
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 148640
89.4%
Latin 17610
 
10.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36699
24.7%
. 33470
22.5%
1 18719
12.6%
2 16061
10.8%
5 6508
 
4.4%
7 6331
 
4.3%
6 6312
 
4.2%
8 6269
 
4.2%
9 6258
 
4.2%
3 5692
 
3.8%
Other values (2) 6321
 
4.3%
Latin
ValueCountFrequency (%)
r 10566
60.0%
E 3522
 
20.0%
o 3522
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36699
22.1%
. 33470
20.1%
1 18719
11.3%
2 16061
9.7%
r 10566
 
6.4%
5 6508
 
3.9%
7 6331
 
3.8%
6 6312
 
3.8%
8 6269
 
3.8%
9 6258
 
3.8%
Other values (5) 19057
11.5%

points_in_wallet
Real number (ℝ)

HIGH CORRELATION 

Distinct23700
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean686.8822
Minimum-760.66124
Maximum2069.0698
Zeros0
Zeros (%)0.0%
Negative136
Negative (%)0.4%
Memory size289.1 KiB
2023-09-19T11:54:03.873507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-760.66124
5-th percentile351.82615
Q1624.35
median686.8822
Q3757.0025
95-th percentile1028.8753
Maximum2069.0698
Range2829.731
Interquartile range (IQR)132.6525

Descriptive statistics

Standard deviation184.81168
Coefficient of variation (CV)0.26905877
Kurtosis5.1876557
Mean686.8822
Median Absolute Deviation (MAD)66.652199
Skewness-0.08432911
Sum25409146
Variance34155.358
MonotonicityNot monotonic
2023-09-19T11:54:03.993616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
686.8821987 3443
 
9.3%
705.07 9
 
< 0.1%
780.92 8
 
< 0.1%
771.75 7
 
< 0.1%
710.69 7
 
< 0.1%
760.54 7
 
< 0.1%
760.76 6
 
< 0.1%
719.78 6
 
< 0.1%
760.58 6
 
< 0.1%
748.98 6
 
< 0.1%
Other values (23690) 33487
90.5%
ValueCountFrequency (%)
-760.6612363 1
< 0.1%
-549.3574977 1
< 0.1%
-506.2567158 1
< 0.1%
-483.8564006 1
< 0.1%
-471.577009 1
< 0.1%
-469.0203988 1
< 0.1%
-445.2884572 1
< 0.1%
-424.6705248 1
< 0.1%
-412.4416878 1
< 0.1%
-405.2670355 1
< 0.1%
ValueCountFrequency (%)
2069.069761 1
< 0.1%
1816.933696 1
< 0.1%
1780.720173 1
< 0.1%
1763.351594 1
< 0.1%
1759.002532 1
< 0.1%
1755.455512 1
< 0.1%
1755.094693 1
< 0.1%
1751.304195 1
< 0.1%
1750.611562 1
< 0.1%
1736.332594 1
< 0.1%

used_special_discount
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
True
20342 
False
16650 
ValueCountFrequency (%)
True 20342
55.0%
False 16650
45.0%
2023-09-19T11:54:04.081943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

offer_application_preference
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
True
20440 
False
16552 
ValueCountFrequency (%)
True 20440
55.3%
False 16552
44.7%
2023-09-19T11:54:04.162726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

past_complaint
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size36.3 KiB
False
18602 
True
18390 
ValueCountFrequency (%)
False 18602
50.3%
True 18390
49.7%
2023-09-19T11:54:04.235391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

feedback
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
Poor Product Quality
6350 
No reason specified
6290 
Too many ads
6279 
Poor Website
6271 
Poor Customer Service
6252 
Other values (4)
5550 

Length

Max length24
Median length21
Mean length17.355455
Min length12

Characters and Unicode

Total characters642013
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProducts always in Stock
2nd rowQuality Customer Care
3rd rowPoor Website
4th rowPoor Website
5th rowPoor Website

Common Values

ValueCountFrequency (%)
Poor Product Quality 6350
17.2%
No reason specified 6290
17.0%
Too many ads 6279
17.0%
Poor Website 6271
17.0%
Poor Customer Service 6252
16.9%
Reasonable Price 1417
 
3.8%
User Friendly Website 1391
 
3.8%
Products always in Stock 1382
 
3.7%
Quality Customer Care 1360
 
3.7%

Length

2023-09-19T11:54:04.323335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T11:54:04.460716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
poor 18873
18.0%
quality 7710
 
7.4%
website 7662
 
7.3%
customer 7612
 
7.3%
product 6350
 
6.1%
reason 6290
 
6.0%
no 6290
 
6.0%
specified 6290
 
6.0%
too 6279
 
6.0%
many 6279
 
6.0%
Other values (11) 25035
23.9%

Most occurring characters

ValueCountFrequency (%)
o 81027
12.6%
67678
 
10.5%
e 62703
 
9.8%
r 52318
 
8.1%
s 39705
 
6.2%
i 38394
 
6.0%
a 33516
 
5.2%
t 32098
 
5.0%
P 28022
 
4.4%
c 23073
 
3.6%
Other values (21) 183479
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 497567
77.5%
Uppercase Letter 76768
 
12.0%
Space Separator 67678
 
10.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 81027
16.3%
e 62703
12.6%
r 52318
10.5%
s 39705
8.0%
i 38394
7.7%
a 33516
 
6.7%
t 32098
 
6.5%
c 23073
 
4.6%
u 23054
 
4.6%
d 21692
 
4.4%
Other values (10) 89987
18.1%
Uppercase Letter
ValueCountFrequency (%)
P 28022
36.5%
C 8972
 
11.7%
Q 7710
 
10.0%
W 7662
 
10.0%
S 7634
 
9.9%
N 6290
 
8.2%
T 6279
 
8.2%
R 1417
 
1.8%
U 1391
 
1.8%
F 1391
 
1.8%
Space Separator
ValueCountFrequency (%)
67678
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 574335
89.5%
Common 67678
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 81027
14.1%
e 62703
 
10.9%
r 52318
 
9.1%
s 39705
 
6.9%
i 38394
 
6.7%
a 33516
 
5.8%
t 32098
 
5.6%
P 28022
 
4.9%
c 23073
 
4.0%
u 23054
 
4.0%
Other values (20) 160425
27.9%
Common
ValueCountFrequency (%)
67678
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 642013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 81027
12.6%
67678
 
10.5%
e 62703
 
9.8%
r 52318
 
8.1%
s 39705
 
6.2%
i 38394
 
6.0%
a 33516
 
5.2%
t 32098
 
5.0%
P 28022
 
4.4%
c 23073
 
3.6%
Other values (21) 183479
28.6%

churn_risk_score
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4633975
Minimum-1
Maximum5
Zeros0
Zeros (%)0.0%
Negative1163
Negative (%)3.1%
Memory size289.1 KiB
2023-09-19T11:54:04.574493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4096609
Coefficient of variation (CV)0.40701679
Kurtosis1.299243
Mean3.4633975
Median Absolute Deviation (MAD)1
Skewness-1.1143052
Sum128118
Variance1.9871439
MonotonicityNot monotonic
2023-09-19T11:54:04.681146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 10424
28.2%
4 10185
27.5%
5 9827
26.6%
2 2741
 
7.4%
1 2652
 
7.2%
-1 1163
 
3.1%
ValueCountFrequency (%)
-1 1163
 
3.1%
1 2652
 
7.2%
2 2741
 
7.4%
3 10424
28.2%
4 10185
27.5%
5 9827
26.6%
ValueCountFrequency (%)
5 9827
26.6%
4 10185
27.5%
3 10424
28.2%
2 2741
 
7.4%
1 2652
 
7.2%
-1 1163
 
3.1%

joining_day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.687122
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size289.1 KiB
2023-09-19T11:54:04.778258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.797726
Coefficient of variation (CV)0.56082475
Kurtosis-1.1982075
Mean15.687122
Median Absolute Deviation (MAD)8
Skewness0.013858918
Sum580298
Variance77.399982
MonotonicityNot monotonic
2023-09-19T11:54:04.875736image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 1285
 
3.5%
7 1263
 
3.4%
8 1259
 
3.4%
24 1247
 
3.4%
12 1245
 
3.4%
27 1243
 
3.4%
19 1236
 
3.3%
2 1233
 
3.3%
17 1232
 
3.3%
13 1231
 
3.3%
Other values (21) 24518
66.3%
ValueCountFrequency (%)
1 1185
3.2%
2 1233
3.3%
3 1230
3.3%
4 1189
3.2%
5 1285
3.5%
6 1219
3.3%
7 1263
3.4%
8 1259
3.4%
9 1195
3.2%
10 1149
3.1%
ValueCountFrequency (%)
31 690
1.9%
30 1113
3.0%
29 1125
3.0%
28 1203
3.3%
27 1243
3.4%
26 1182
3.2%
25 1183
3.2%
24 1247
3.4%
23 1227
3.3%
22 1226
3.3%

joining_month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5334397
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size289.1 KiB
2023-09-19T11:54:04.981483image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4501303
Coefficient of variation (CV)0.52807257
Kurtosis-1.2037093
Mean6.5334397
Median Absolute Deviation (MAD)3
Skewness-0.01362175
Sum241685
Variance11.903399
MonotonicityNot monotonic
2023-09-19T11:54:05.079472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 3194
8.6%
7 3186
8.6%
1 3158
8.5%
8 3147
8.5%
10 3095
8.4%
4 3079
8.3%
6 3076
8.3%
3 3068
8.3%
9 3061
8.3%
5 3061
8.3%
Other values (2) 5867
15.9%
ValueCountFrequency (%)
1 3158
8.5%
2 2844
7.7%
3 3068
8.3%
4 3079
8.3%
5 3061
8.3%
6 3076
8.3%
7 3186
8.6%
8 3147
8.5%
9 3061
8.3%
10 3095
8.4%
ValueCountFrequency (%)
12 3194
8.6%
11 3023
8.2%
10 3095
8.4%
9 3061
8.3%
8 3147
8.5%
7 3186
8.6%
6 3076
8.3%
5 3061
8.3%
4 3079
8.3%
3 3068
8.3%

joining_year
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size289.1 KiB
2017
12540 
2015
12297 
2016
12155 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters147968
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2016
4th row2016
5th row2017

Common Values

ValueCountFrequency (%)
2017 12540
33.9%
2015 12297
33.2%
2016 12155
32.9%

Length

2023-09-19T11:54:05.191019image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T11:54:05.279791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
2017 12540
33.9%
2015 12297
33.2%
2016 12155
32.9%

Most occurring characters

ValueCountFrequency (%)
2 36992
25.0%
0 36992
25.0%
1 36992
25.0%
7 12540
 
8.5%
5 12297
 
8.3%
6 12155
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 147968
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 36992
25.0%
0 36992
25.0%
1 36992
25.0%
7 12540
 
8.5%
5 12297
 
8.3%
6 12155
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Common 147968
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 36992
25.0%
0 36992
25.0%
1 36992
25.0%
7 12540
 
8.5%
5 12297
 
8.3%
6 12155
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 36992
25.0%
0 36992
25.0%
1 36992
25.0%
7 12540
 
8.5%
5 12297
 
8.3%
6 12155
 
8.2%

last_visit_time_hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.537711
Minimum0
Maximum23
Zeros1512
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size144.6 KiB
2023-09-19T11:54:05.359976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9215733
Coefficient of variation (CV)0.59990872
Kurtosis-1.202568
Mean11.537711
Median Absolute Deviation (MAD)6
Skewness-0.0076141329
Sum426803
Variance47.908177
MonotonicityNot monotonic
2023-09-19T11:54:05.456457image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
11 1603
 
4.3%
13 1585
 
4.3%
17 1582
 
4.3%
2 1569
 
4.2%
22 1566
 
4.2%
16 1565
 
4.2%
20 1562
 
4.2%
8 1561
 
4.2%
1 1559
 
4.2%
21 1559
 
4.2%
Other values (14) 21281
57.5%
ValueCountFrequency (%)
0 1512
4.1%
1 1559
4.2%
2 1569
4.2%
3 1478
4.0%
4 1547
4.2%
5 1499
4.1%
6 1533
4.1%
7 1516
4.1%
8 1561
4.2%
9 1528
4.1%
ValueCountFrequency (%)
23 1544
4.2%
22 1566
4.2%
21 1559
4.2%
20 1562
4.2%
19 1533
4.1%
18 1504
4.1%
17 1582
4.3%
16 1565
4.2%
15 1547
4.2%
14 1508
4.1%

last_visit_time_minutes
Real number (ℝ)

ZEROS 

Distinct60
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.634353
Minimum0
Maximum59
Zeros645
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size144.6 KiB
2023-09-19T11:54:05.560351image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q115
median30
Q345
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.300883
Coefficient of variation (CV)0.58381172
Kurtosis-1.1956266
Mean29.634353
Median Absolute Deviation (MAD)15
Skewness-0.008682063
Sum1096234
Variance299.32054
MonotonicityNot monotonic
2023-09-19T11:54:05.673241image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 667
 
1.8%
34 667
 
1.8%
41 663
 
1.8%
28 658
 
1.8%
17 657
 
1.8%
50 655
 
1.8%
52 653
 
1.8%
30 651
 
1.8%
58 648
 
1.8%
46 647
 
1.7%
Other values (50) 30426
82.3%
ValueCountFrequency (%)
0 645
1.7%
1 584
1.6%
2 626
1.7%
3 621
1.7%
4 578
1.6%
5 571
1.5%
6 629
1.7%
7 606
1.6%
8 577
1.6%
9 614
1.7%
ValueCountFrequency (%)
59 604
1.6%
58 648
1.8%
57 625
1.7%
56 622
1.7%
55 632
1.7%
54 588
1.6%
53 630
1.7%
52 653
1.8%
51 600
1.6%
50 655
1.8%

last_visit_time_seconds
Real number (ℝ)

ZEROS 

Distinct60
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.575205
Minimum0
Maximum59
Zeros630
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size144.6 KiB
2023-09-19T11:54:05.788088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q114
median30
Q345
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)31

Descriptive statistics

Standard deviation17.415587
Coefficient of variation (CV)0.58885768
Kurtosis-1.2113213
Mean29.575205
Median Absolute Deviation (MAD)15
Skewness-0.0099451035
Sum1094046
Variance303.30266
MonotonicityNot monotonic
2023-09-19T11:54:05.909515image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 673
 
1.8%
32 671
 
1.8%
4 659
 
1.8%
49 654
 
1.8%
50 652
 
1.8%
47 650
 
1.8%
2 649
 
1.8%
51 647
 
1.7%
10 640
 
1.7%
27 639
 
1.7%
Other values (50) 30458
82.3%
ValueCountFrequency (%)
0 630
1.7%
1 625
1.7%
2 649
1.8%
3 624
1.7%
4 659
1.8%
5 622
1.7%
6 576
1.6%
7 621
1.7%
8 629
1.7%
9 634
1.7%
ValueCountFrequency (%)
59 637
1.7%
58 627
1.7%
57 614
1.7%
56 631
1.7%
55 626
1.7%
54 624
1.7%
53 610
1.6%
52 622
1.7%
51 647
1.7%
50 652
1.8%

Interactions

2023-09-19T11:53:57.107394image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:47.311777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:48.291189image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:49.250910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:50.207226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:51.419292image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:52.343152image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:53.292102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:54.205701image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:55.195359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:56.107937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:57.188258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:47.424388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:48.369375image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:49.348400image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:50.295368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:51.499584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:52.439274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:53.372724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:54.293792image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:55.284660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:56.188280image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:57.631639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:47.512298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:48.450469image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:49.437003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:50.375632image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:51.588589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:52.519855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:53.453475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:54.373318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:55.365354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:56.268701image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:57.727652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:47.601317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:48.545996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:49.517012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:50.464838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:51.679691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:52.592541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:53.542433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:54.479520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:55.452843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:56.364990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:57.825903image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:47.681981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:48.626489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:49.598808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:50.552741image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:51.760117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:52.685424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:53.624109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:54.578659image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:55.548707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:56.485924image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:57.921933image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:47.762822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:48.714862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:49.680682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:50.629898image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:51.846083image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:52.774784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:53.704300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:54.659326image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:55.629969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:56.574633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:58.036327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:47.859138image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:48.811050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:49.768673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:50.712636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:51.926214image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:52.856963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:53.784919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:54.757648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:55.710644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:56.670726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:58.142809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:47.939499image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:48.891006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:49.860044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:50.812875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:52.014252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:52.943986image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:53.865997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:54.847287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:55.794489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:56.751293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:58.236913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:48.026664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:48.979494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:49.941221image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:51.162121image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:52.094106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:53.024006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:53.949058image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:54.936857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:55.866704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:56.853865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:58.314446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:48.104215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:49.062568image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:50.022349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:51.242631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:52.166485image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:53.121022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:54.029321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:55.024962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:55.939119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:56.931634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:58.401923image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:48.201299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:49.144657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:50.118363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:51.322718image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:52.255731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:53.202542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:54.117449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:55.106286image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:56.027508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-09-19T11:53:57.018839image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-09-19T11:54:06.005677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
agedays_since_last_loginavg_time_spentavg_transaction_valuepoints_in_walletchurn_risk_scorejoining_dayjoining_monthlast_visit_time_hourlast_visit_time_minuteslast_visit_time_secondsgenderregion_categorymembership_categoryjoined_through_referralpreferred_offer_typesmedium_of_operationinternet_optionused_special_discountoffer_application_preferencepast_complaintfeedbackjoining_year
age1.000-0.0030.003-0.001-0.0010.0040.0000.005-0.014-0.002-0.0070.0000.0000.0000.0030.0070.0080.0140.0000.0000.0120.0060.010
days_since_last_login-0.0031.000-0.100-0.005-0.0010.0160.010-0.0090.005-0.0030.0020.0080.0000.0000.0080.0040.0000.0000.0000.0000.0000.0120.004
avg_time_spent0.003-0.1001.0000.0190.011-0.029-0.0060.004-0.004-0.007-0.0060.0000.0120.0040.0880.0070.2090.0100.1130.1040.0000.0210.000
avg_transaction_value-0.001-0.0050.0191.0000.105-0.2010.006-0.0030.007-0.0100.0030.0000.0240.1310.0330.0390.0230.0020.0000.0350.0000.2440.000
points_in_wallet-0.001-0.0010.0110.1051.000-0.542-0.002-0.0080.0010.0100.0040.0000.0060.2050.0060.0110.0000.0000.0100.0030.0000.0830.000
churn_risk_score0.0040.016-0.029-0.201-0.5421.0000.0060.008-0.0070.002-0.0000.0000.0340.4130.0470.0640.0270.0000.0060.0500.0110.4410.004
joining_day0.0000.010-0.0060.006-0.0020.0061.0000.005-0.0020.0020.0020.0000.0100.0100.0000.0020.0000.0000.0080.0000.0000.0060.000
joining_month0.005-0.0090.004-0.003-0.0080.0080.0051.0000.001-0.0070.0020.0000.0000.0000.0070.0000.0140.0000.0000.0060.0000.0080.000
last_visit_time_hour-0.0140.005-0.0040.0070.001-0.007-0.0020.0011.000-0.0050.0000.0000.0000.0070.0090.0000.0070.0000.0090.0140.0000.0000.000
last_visit_time_minutes-0.002-0.003-0.007-0.0100.0100.0020.002-0.007-0.0051.0000.0030.0070.0000.0000.0080.0000.0000.0080.0100.0000.0000.0060.012
last_visit_time_seconds-0.0070.002-0.0060.0030.004-0.0000.0020.0020.0000.0031.0000.0070.0060.0030.0000.0000.0060.0000.0000.0000.0000.0040.000
gender0.0000.0080.0000.0000.0000.0000.0000.0000.0000.0070.0071.0000.0000.0000.0000.0060.0020.0000.0000.0000.0040.0030.000
region_category0.0000.0000.0120.0240.0060.0340.0100.0000.0000.0000.0060.0001.0000.0130.0000.0040.0000.0000.0000.0000.0000.0330.004
membership_category0.0000.0000.0040.1310.2050.4130.0100.0000.0070.0000.0030.0000.0131.0000.0220.0240.0130.0030.0040.0110.0050.1880.010
joined_through_referral0.0030.0080.0880.0330.0060.0470.0000.0070.0090.0080.0000.0000.0000.0221.0000.0000.0440.0040.0190.0210.0000.0470.000
preferred_offer_types0.0070.0040.0070.0390.0110.0640.0020.0000.0000.0000.0000.0060.0040.0240.0001.0000.0000.0000.0000.0000.0000.0640.000
medium_of_operation0.0080.0000.2090.0230.0000.0270.0000.0140.0070.0000.0060.0020.0000.0130.0440.0001.0000.0000.0630.0490.0050.0280.000
internet_option0.0140.0000.0100.0020.0000.0000.0000.0000.0000.0080.0000.0000.0000.0030.0040.0000.0001.0000.0000.0000.0000.0050.000
used_special_discount0.0000.0000.1130.0000.0100.0060.0080.0000.0090.0100.0000.0000.0000.0040.0190.0000.0630.0001.0000.8140.0050.0080.005
offer_application_preference0.0000.0000.1040.0350.0030.0500.0000.0060.0140.0000.0000.0000.0000.0110.0210.0000.0490.0000.8141.0000.0050.0490.008
past_complaint0.0120.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0040.0000.0050.0000.0000.0050.0000.0050.0051.0000.0100.006
feedback0.0060.0120.0210.2440.0830.4410.0060.0080.0000.0060.0040.0030.0330.1880.0470.0640.0280.0050.0080.0490.0101.0000.000
joining_year0.0100.0040.0000.0000.0000.0040.0000.0000.0000.0120.0000.0000.0040.0100.0000.0000.0000.0000.0050.0080.0060.0001.000

Missing values

2023-09-19T11:53:58.602960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-19T11:53:58.979403image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idNameagegendersecurity_noregion_categorymembership_categoryjoined_through_referralreferral_idpreferred_offer_typesmedium_of_operationinternet_optiondays_since_last_loginavg_time_spentavg_transaction_valueavg_frequency_login_dayspoints_in_walletused_special_discountoffer_application_preferencepast_complaintfeedbackchurn_risk_scorejoining_dayjoining_monthjoining_yearlast_visit_time_hourlast_visit_time_minuteslast_visit_time_seconds
0fffe4300490044003600300030003800Pattie Morrisey18FXW0DQ7HVillagePlatinum MembershipNoxxxxxxxxGift Vouchers/Coupons?Wi-Fi17300.6353005.2517.0781.750000YesYesNoProducts always in Stock217820171682
1fffe43004900440032003100300035003700Traci Peery32F5K0N3X1CityPremium Membership?CID21329Gift Vouchers/CouponsDesktopMobile_Data16306.3412838.3810.0686.882199YesNoYesQuality Customer Care12882017123813
2fffe4300490044003100390032003600Merideth Mcmeen44F1F2TCL3TownNo MembershipYesCID12313Gift Vouchers/CouponsDesktopWi-Fi14516.1621027.0022.0500.690000NoYesYesPoor Website511112016225321
3fffe43004900440036003000330031003600Eufemia Cardwell37MVJGJ33NCityNo MembershipYesCID3793Gift Vouchers/CouponsDesktopMobile_Data1153.2725239.566.0567.660000NoYesYesPoor Website529102016155750
4fffe43004900440031003900350030003600Meghan Kosak31FSVZXCWBCityNo MembershipNoxxxxxxxxCredit/Debit Card OffersSmartphoneMobile_Data20113.1324483.6616.0663.060000NoYesYesPoor Website51292017154644
5fffe43004900440036003300320035003300Leslie Browder13MPSG1LGFCityGold MembershipNoxxxxxxxxGift Vouchers/Coupons?Wi-Fi23433.6213884.7724.0722.270000YesNoYesNo reason specified38120166467
6fffe43004900440031003100360037003900Bridget Balog21MR3CX1EATownGold MembershipYesCID24708Gift Vouchers/CouponsDesktopMobile_Data1055.388982.5028.0756.210000YesNoYesNo reason specified3193201511404
7fffe4300490044003800300035003800Herma Torgeson42M4UJ1551TownNo Membership?CID56614Credit/Debit Card OffersBothFiber_Optic19429.1144554.8224.0568.080000NoYesYesPoor Product Quality5127201675243
8fffe43004900440033003300330032003200Pattie Helmers44M0481QNQVillageSilver MembershipNoxxxxxxxxWithout OffersSmartphoneFiber_Optic15191.0718362.3120.0686.882199YesNoYesPoor Customer Service31412201665010
9fffe43004900440032003000340038003300Shaquana Leech45FZHP4MCRTownNo MembershipNoxxxxxxxxGift Vouchers/Coupons?Wi-Fi1097.3119244.1628.0706.230000NoYesYesPoor Customer Service430112016191016
customer_idNameagegendersecurity_noregion_categorymembership_categoryjoined_through_referralreferral_idpreferred_offer_typesmedium_of_operationinternet_optiondays_since_last_loginavg_time_spentavg_transaction_valueavg_frequency_login_dayspoints_in_walletused_special_discountoffer_application_preferencepast_complaintfeedbackchurn_risk_scorejoining_dayjoining_monthjoining_yearlast_visit_time_hourlast_visit_time_minuteslast_visit_time_seconds
36982fffe43004900440033003600330033003800Leslie Bruneau45FI2TAL7NTownPremium MembershipNoxxxxxxxxGift Vouchers/Coupons?Wi-Fi1034.93000041558.9319.0703.030000YesNoNoPoor Product Quality3318201683041
36983fffe43004900440032003300370030003700Faustina Balog45MPU0XLQYTownBasic MembershipYesCID45477Without OffersSmartphoneWi-Fi949.33000045358.4911.0242.979625YesNoNoPoor Customer Service53082016105331
36984fffe43004900440035003800320035003300Hilary Ortego51MLM92BDSTownGold MembershipNoxxxxxxxxWithout OffersDesktopFiber_Optic24312.33000063446.712.0778.700000NoYesNoProducts always in Stock17102016154136
36985fffe4300490044003800310034003500Dwain Cann12FGWAHGJYVillagePremium MembershipNoxxxxxxxxGift Vouchers/CouponsDesktopFiber_Optic13418.38000056397.217.0725.890000YesYesYesProducts always in Stock22510201633017
36986fffe43004900440034003900300036003500Marlena Chastain27M8X0LUUSTownPlatinum MembershipYesCID15800Credit/Debit Card OffersDesktopMobile_Data13135.8300008225.6816.0748.570000YesNoNoNo reason specified379201552919
36987fffe43004900440035003500390036003100Cuc Tarr46F6F51HFOTownBasic MembershipNoxxxxxxxxCredit/Debit Card OffersDesktopWi-Fi2-650.68275927277.686.0639.510000NoYesYesNo reason specified421920174145
36988fffe43004900440033003500380036003600Jenni Stronach29F21KSM8YTownBasic MembershipNoxxxxxxxxWithout OffersSmartphoneWi-Fi13-638.12342111069.7128.0527.990000YesNoNoPoor Customer Service52762016231831
36989fffe4300490044003500330034003100Luciana Kinch23FXK1IM9HTownBasic MembershipYesCID3838Gift Vouchers/CouponsDesktopWi-Fi12154.94000038127.56Error680.470000NoYesYesPoor Website4119201635025
36990fffe43004900440031003200390039003000Tawana Ardoin53MK6VTP1ZVillagePlatinum MembershipNoxxxxxxxxGift Vouchers/CouponsSmartphoneMobile_Data15482.6100002378.8620.0197.264414YesYesNoNo reason specified315620179503
36991fffe43004900440033003600340034003200Verlene Beaulieu35MLBX0GLRTownSilver MembershipNoxxxxxxxxGift Vouchers/CouponsDesktopMobile_Data1579.1800002189.68Error719.970000YesNoNoQuality Customer Care22310201513952